Lars Schmidt-Thieme

Time series models can achieve state-of-the-art long-term forecasting by dynamically blending recent dynamics with retrieved historical trajectories, rather than relying solely on a single look-back window.

Recurrently folding older time series context onto a most-recent anchor dramatically improves forecasting accuracy by naturally prioritizing recent evidence.

Achieving state-of-the-art accuracy in time series forecasting without sacrificing mean preservation or requiring costly sampling methods is now possible with TORF.

TFMs, despite being the leading approach for tabular predictions, fail to consistently represent joint distributions, raising concerns about their reliability in practical applications.

Forget unreliable forecasts: CircuITS offers structurally guaranteed valid joint distributions for irregular multivariate time series, outperforming existing methods in joint and marginal density estimation.